As more applications and services (hereinafter “services”) have become commonly available over the World Wide Web portion of the Internet (the “Web”), the number, type and complexity of resources required to support access to such services have increased. In addition, the complexity of the relationships between such resources, and the complexity of the algorithms employed to allocate such resources has increased.
Many web-based services require service consumers to register for access to a service. Such registering requires that resources be allocated to such registering consumers. Registering the increasing numbers of consumers has become more difficult because of factors including, but not limited to, the number, type, sophistication and complexity of relationships between the resources required to support such consumers coupled with the volume of consumers registering for services, the rate at which new consumers are registering for services and the volume of services available. Conventionally registering new consumers, which may include allocating one or more accounts for the new service consumers, may have required manual intervention (e.g., substituting/adding/removing resources, updating allocation algorithms, updating resource information). Such manual intervention can be time-consuming, complicated and expensive, producing resource allocation delays (and resulting registration delays) or allocation errors that negatively impact allocating resources. Consumers forced to wait for access to services may forego using the service, thus resulting in lost business for the service provider. Thus, automating consumer registration is required.
But automating consumer registration creates new problems. For example, due to the increasing number, type, complexity and relationships between resources, it has become increasingly difficult to efficiently allocate resources for consumers registering to use services available over the Web. Conventionally, allocating resources may have required a manual intervention, or the application of an allocation rule or allocation algorithm. Such interventions, allocation rules and algorithms may have required accessing information concerning resources. But the resource information, allocation rules and/or allocation algorithms may have been static, and unable to respond to the changing number, type, complexity and relationships between the resources to be allocated. The information, allocation rules and/or allocation algorithms may have become static because they were hard coded into data structures and/or programs that were inflexible and difficult to change, in some cases requiring rewriting programming code in order to change allocation algorithms.
Manual allocation and/or inflexible allocation rules may generate delays in resource allocation and/or wasteful resource allocation. For example, an allocation rule that dedicates a standard set of resources to a user registering for an application may over-allocate or under-allocate resources to that user. Such standard allocations may be artifacts of inflexible allocation rules and/or algorithms. By way of illustration, a first consumer registering for an email service may be allocated 10 Mb of disk space, but the first consumer may only undertake processing that consumes 1 Mb of disk space, thus leaving 9 Mb of disk space underutilized and/or over-allocated. The first consumer will likely not be negatively impacted by the over-allocation. But a second consumer may have 10 Mb of disk space allocated and may undertake processing that could consume 15 Mb of disk space if 15 Mb of disk space was available. The second consumer will likely be negatively impacted by both the over-allocation and the under-allocation, noticing slow and/or unsuccessful access to the service. Conventionally, adapting allocations to such consumers, if possible, may have required manual intervention. Further, adapting the allocation rules and/or algorithms employed to allocate resources to such users, if possible, may have similarly required manual intervention. Thus, such adaptations may have been foregone, resulting in inappropriate allocations and/or allocation algorithms.
Conventionally, resources for a service may have been allocated from a single logical resource. For example, all consumers of an email service may have had resources allocated by a single email resource allocation server. As the number, type, complexity and relationships between resources increases, and as the rate and volume of registering consumers increases, it becomes increasingly difficult to allocate resources from a single resource server. It has been difficult to coordinate allocating resources from multiple resource servers because of problems including, but not limited to, disparities in resource allocation algorithms, disparities in resource allocation rules, different resource descriptions and contention/timing problems.
The size and/or characteristics of services available over the Web can change, resulting in corresponding changes in resource allocation demands, which further increases complexity problems associated with resource allocation. Thus, even if a conventional allocation method was able to allocate resources for a service, it may not be able to allocate resources when the service changes size and/or characteristics (e.g., number and type of resources required) due to the increased allocation complexity.
Allocating resources to consumers is further complicated because the resources available to be allocated may change. By way of illustration, the number and type of resources available may grow or diminish at the same time that the characteristics of the resources are changing. Further, the relationships between resources to be allocated can also change. For example, at a first point in time, 100 disks of 100 Mb each may be available, but at a second point in time 75 disks of 350 Mb each may be available. Thus, static rules for resource allocation may become obsolete and lead to unsatisfactory resource allocation. While the changing numbers and characteristics of resources complicate resource allocation, adding additional services that require different allocation algorithms for the available resources can add even further complexity to resource allocation. Similarly, changing the resource mix required by a service can generate even more complexity.
Conventionally monitoring and reallocating resources, if performed at all, may have been accomplished manually and/or by inflexible programs. Thus, determining that a new resource was required, or that a new relationship between resources exists, or adapting to the new resource or relationship, if possible, may have required the manual intervention of a skilled resource allocator or the application of an outdated program. As the complexity of allocating resources to the ever-increasing number of services increases, such resource allocators can easily become overwhelmed by the volume and complexity of the resource allocation tasks and such programs can quickly produce inappropriate allocations.